Senior Data Scientist - Geospatial ML in Production

GRVTY; Chantilly, Northern (VA, KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

GRVTY is seeking a Senior Data Scientist to shape an enterprise-scale analytics framework for geospatial intelligence. You will own end-to-end ML workflows, from data preprocessing to production models, working with government stakeholders to ensure mission relevance.

This role emphasizes building robust pipelines, applying ML and statistics to large geospatial imagery datasets, and communicating complex methods clearly to non-technical customers.

Qualifications

• Active Top Secret clearance with SCI/CI Polygraph.
• Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• 9+ years of professional data science experience with production-quality deliverables.
• Strong Python programming skills with NumPy, pandas, scikit-learn, SciPy.

Responsibilities

• Develop and apply ML, statistical, and data mining techniques to geospatial imagery and large-scale datasets.
• Design automated analytical workflows and IV&V evaluation pipelines for GEOINT products.
• Build production-grade data pipelines for ingesting, transforming, and processing multi-source data.
• Collaborate with software engineers, architects, and government stakeholders to ensure usable outputs.
• Evaluate output quality, identify failure modes, and improve metric accuracy.
• Document methodologies and validation approaches to support program continuity.

Skills

Python

ML & Stats

Data analysis

Stakeholder comms

Education

Bachelor's degree in DS/CS/Math/Stats

Tools

NumPy

pandas

scikit-learn

SciPy

GeoPandas

Shapely

Rasterio

GDAL

Job description

GRVTY is seeking a Senior Data Scientist to shape an enterprise-scale analytics framework for geospatial intelligence. You will own end-to-end ML workflows, from data preprocessing to production models, working with government stakeholders to ensure mission relevance.

This role emphasizes building robust pipelines, applying ML and statistics to large geospatial imagery datasets, and communicating complex methods clearly to non-technical customers.